This paper proposes a digital video intrusion detection method based on Narrow Band Internet of Things (NB-IoT), and establishes a digital video intrusion detection system based on NB-IoT network and SVM intelligent classification algorithm. Firstly, the image is preprocessed by gradation processing and threshold transformation to extract the HOG feature extraction of human intrusion behavior in digital video frame images. Then, combined with the human intrusion HOG feature data, the SVM intelligent algorithm is used to classify the human intrusion behavior, so as to accurately classify the movements of walking, jumping, running and waving in video surveillance. Finally, the performance analysis of the algorithm finds that the classification time, classification accuracy and classification false positive rate of the model are tested. The classification time is 40.8 s, the shortest is 27 s, the classification accuracy is 87.65%, and the lowest is 83.7%. The false detection rate is up to 15%, both of which are less than 20%, indicating that the classification method has good accuracy and stability. Comparing the algorithm with other algorithms, the intrusion sensitivity, intrusion specificity and training speed of the model are 93.6%, 94.3%, and 19 s, respectively, which are better than other methods, which indicates that the model has good detection performance in the experimental stage.

提出了一种基于窄带物联网的数字视频入侵检测方法，并建立了基于NB-IoT网络和SVM智能分类算法的数字视频入侵检测系统。首先，通过灰度处理和阈值变换对图像进行预处理，以提取数字视频帧图像中人为入侵行为的HOG特征提取。然后，结合人为入侵HOG特征数据，采用SVM智能算法对人为入侵行为进行分类，从而对视频监控中行走，跳跃，奔跑和挥舞的动作进行准确分类。最后，通过对算法的性能分析，验证了模型的分类时间，分类精度和分类误报率。分类时间为40.8 s，最短为27 s，分类精度为87.65％，最低为83.7％。误检率高达15％，均小于20％，表明分类方法具有良好的准确性和稳定性。与其他算法相比，该模型的入侵敏感性，入侵特异性和训练速度分别为93.6％，94.3％和19 s，优于其他方法，表明该模型在入侵检测中具有良好的检测性能。实验阶段。